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Volume 4 Issue 3
Jul.  2017

IEEE/CAA Journal of Automatica Sinica

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Chuanwei Liu, Yunfa Fu, Jun Yang, Xin Xiong, Huiwen Sun and Zhengtao Yu, "Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 551-557, July 2017. doi: 10.1109/JAS.2016.7510121
Citation: Chuanwei Liu, Yunfa Fu, Jun Yang, Xin Xiong, Huiwen Sun and Zhengtao Yu, "Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 551-557, July 2017. doi: 10.1109/JAS.2016.7510121

Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy

doi: 10.1109/JAS.2016.7510121
Funds:

the National Natural Science Foundation of China 81470084

the National Natural Science Foundation of China 61463024

the Research Project for Application Foundation of Yunnan Province 2013FB026

the Cultivation Program of Talents of Yunnan Province KKSY201303048

the Focal Program for Education Department of Yunnan Province 2013Z130

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  • Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles. This is a new strategy of human-computer interaction. A method of electroencephalogram (EEG) phase synchronization combined with band energy was proposed to construct a feature vector for pattern recognition of brain-computer interaction based on EEG induced by motor imagery in this paper. rhythm and beta rhythm were first extracted from EEG by band pass filter and then the frequency band energy was calculated by the sliding time window; the instantaneous phase values were obtained using Hilbert transform and then the phase synchronization feature was calculated by the phase locking value (PLV) and the best time interval for extracting the phase synchronization feature was searched by the distribution of the PLV value in the time domain. Finally, discrimination of motor imagery patterns was performed by the support vector machine (SVM). The results showed that the phase synchronization feature more effective in 4 s-7 s and the correct classification rate was 91.4 %. Compared with the results achieved by a single EEG feature related to motor imagery, the correct classification rate was improved by 3.5 and 4.3 percentage points by combining phase synchronization with band energy. These indicate that the proposed method is effective and it is expected that the study provides a way to improve the performance of the online real-time brain-computer interaction control system based on EEG related to motor imagery.

     

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  • [1]
    M. Barinaga, "Turning thoughts into actions, " Science, vol. 286, no. 5441, pp. 888-890, Oct. 1999. http://www.jstor.org/stable/2899467
    [2]
    S. H. Scott, "Neuroscience: Converting thoughts into action, " Nature, vol. 442, no. 7099, pp. 141-142, Jul. 2006. http://www.ncbi.nlm.nih.gov/pubmed/16838004
    [3]
    L. R. Hochberg, "Turning thought into action, " N. Engl. J. Med. , vol. 359, no. 11, pp. 1175-1177, Sep. 2008. http://www.ncbi.nlm.nih.gov/pubmed/18784110
    [4]
    D. Tan and A. Nijholt, "Brain-computer interfaces and human-computer interaction, " in Brain-Computer Interfaces, D. S. Tan and A. Nijholt, Eds. , London, UK: Springer, 2010. https://www.researchgate.net/publication/44254855_Preface_to_Brain-Computer_Interfaces_Applying_our_Minds_to_Human-Computer_Interaction
    [5]
    J. J. Vidal, "Toward direct brain-computer communication, " Annu. Rev. Biophys. Bioeng. , vol. 2, no. 1, pp. 157-180, Jun. 1973. https://www.ncbi.nlm.nih.gov/pubmed/4583653
    [6]
    J. J. Vidal, "Real-time detection of brain events in EEG, " Proc. IEEE, vol. 65, no. 5, pp. 633-641, May 1977. http://ieeexplore.ieee.org/document/1454811/
    [7]
    B. Blankertz, M. Tangermann, C. Vidaurre, S. Fazli, C. Sannelli, S. Haufe, C. Maeder, L. Ramsey, I. Sturm, G. Curio, and K. R. Muller, "The Berlin brain-computer interface: non-medical uses of BCI technology, " Front. Neurosci. , vol. 4, Article ID 198, Dec. 2010.
    [8]
    Y. F. Fu, Y. C. Wang, H. Y. Li, B. L. Xu, and Y. C. Li, "Direct braincontrolled robot interface technology, " Acta Automat. Sin. , vol. 38, no. 8, pp. 1229-1246, Jun. 2012. https://www.researchgate.net/publication/257137793_Direct_Brain-controlled_Robot_Interface_Technology
    [9]
    J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan, "Brain-computer interface technology: a review of the first international meeting, " IEEE Trans. Rehabil. Eng. , vol. 8, no. 2, pp. 164-173, Jun. 2000. https://cwru.pure.elsevier.com/en/publications/brain-computer-interface-technology-a-review-of-the-first-interna-2
    [10]
    J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control, " Clin. Neurophysiol. , vol. 113, no. 6, pp. 767-791, Jun. 2002. http://psycnet.apa.org/psycinfo/2002-15205-001
    [11]
    G. Pfurtscheller, B. Z. Allison, C. Brunner, G. Bauernfeind, T. SolisEscalante, R. Scherer, T. O. Zander, G. Mueller-Putz, C. Neuper, and N. Birbaumer, "The hybrid BCI, " Front. Neurosci. , vol. 4, Article ID 30, Apr. 2010. http://www.ncbi.nlm.nih.gov/pubmed/20582271/
    [12]
    B. Z. Allison, C. Brunner, V. Kaiser, G. R. Muller-Putz, C. Neuper, and G. Pfurtscheller, "Toward a hybrid brain-computer interface based on imagined movement and visual attention, " J. Neural. Eng. , vol. 7, no. 2, Article ID 026007, Mar. 2010. https://www.ncbi.nlm.nih.gov/pubmed/20332550/
    [13]
    S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K. R. Muller, and B. Blankertz, "Enhanced performance by a hybrid NIRS-EEG brain computer interface, " NeuroImage, vol. 59, no. 1, pp. 519-529, Jan. 2012. http://www.sciencedirect.com/science/article/pii/S1053811911008792
    [14]
    X. X. Yin, B. L. Xu, C. H. Jiang, Y. F. Fu, Z. D. Wang, H. Y. Li, and G. Shi, "A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching, " J. Neural Eng. , vol. 12, no. 3, Article ID 036004, Apr. 2015. https://www.ncbi.nlm.nih.gov/pubmed/25834118
    [15]
    Y. J. Wang, B. Hong, X. R. Gao, and S. K. Gao, "Integrating power and phase features for multi-class motor imagery based Brian-computer interface, " in Proceedings of 2007 Joint Academic Annual Conference of China Biomedical Engineering, Xi'an, China, 2007. https://www.researchgate.net/publication/5844143_Implementation_of_a_Brain-Computer_Interface_Based_on_Three_States_of_Motor_Imagery
    [16]
    G. Pfurtscheller, C. Brunner, A. Schlogl, and F. H. Lopes da Silva, "Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks, " NeuroImage, vol. 31, no. 1, pp. 153-159, May 2006. http://www.ncbi.nlm.nih.gov/pubmed/16443377
    [17]
    G. Pfurtscheller and F. H. Lopes da Silva, "Event-related EEG/MEG synchronization and desynchronization: basic principles, " Clin. Neurophysiol. , vol. 110, no. 11, pp. 1842-1857, Nov. 1999. http://www.sciencedirect.com/science/article/pii/S1388245799001418
    [18]
    G. Pfurtscheller and C. Neuper, "Motor imagery and direct braincomputer communication, " Proc. IEEE, vol. 89, no. 7, pp. 1123-1134, Jul. 2001. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=939829
    [19]
    C. Neuper, M. Wortz, G. Pfurtscheller, "ERD/ERS patterns reflecting sensorimotor activation and deactivation, " Prog. Brain Res. , vol. 159, pp. 211-222, Dec. 2006. http://www.sciencedirect.com/science/article/pii/S0079612306590144
    [20]
    G. Santhanam, S. I. Ryu, B. M. Yu, A. Afshar, and K. V. Shenoy, "A high-performance brain-computer interface, " Nature, vol. 442, no. 7099, pp. 195-198, Jul. 2006. http://www.ncbi.nlm.nih.gov/pubmed/16838020/
    [21]
    H. W. Sun, Y. F. Fu, R. Xiong, C. W. Liu, and Z. T. Yu, "Identification of EEG induced by motor imagery based on Hilbert-Huang transform, " Acta Automat. Sin. , vol. 41, no. 9, pp. 1686-1692, Sep. 2015. https://www.researchgate.net/publication/283689113_Identification_of_EEG_induced_by_motor_imagery_based_on_Hilbert-Huang_transform
    [22]
    Y. Gu, K. Dremstrup, and D. Farina, "Single-trial discrimination of type and speed of wrist movements from EEG recordings, " Clin. Neurophysiol. , vol. 120, no. 8, pp. 1596-600, Aug. 2009. http://europepmc.org/abstract/med/19535289
    [23]
    A. Spiegler, B. Graimann, and G. Pfurtscheller, "Phase coupling between different motor areas during tongue-movement imagery, " Neurosci. Lett. , vol. 369, no. 1, pp. 50-54, Oct. 2004. http://www.ncbi.nlm.nih.gov/pubmed/15380306
    [24]
    C. Carmeli, M. G. Knyazeva, G. M. Innocenti, and O. De Feo, "Assessment of EEG synchronization based on state-space analysis, " Neuroimage, vol. 25, no. 2, pp. 339-354, Apr. 2005. http://europepmc.org/abstract/MED/15784413
    [25]
    S. Lemm, C. Schafer, and G. Curio, "BCI competition 2003-data set Ⅲ: probabilistic modeling of sensorimotor µ rhythms for classification of imaginary hand movements, " IEEE Trans. Biomed. Eng. , vol. 51, no. 6, pp. 1077-1080, May 2004. http://www.ncbi.nlm.nih.gov/pubmed/15188882/
    [26]
    "BCI Competition Ⅳ"[Online]. Available: http://www.bbci.de/competition/iv/. Accessed on: August 13, 2015.
    [27]
    K. Toma, T. Mima, T. Matsuoka, C. Gerloff, T. Ohnishi, B. Koshy, F. Andres, and M. Hallett, "Movement rate effect on activation and functional coupling of motor cortical areas, " J. Neurophysiol. , vol. 88, no. 6, pp. 3377-3385, Dec. 2002. http://europepmc.org/abstract/MED/12466454
    [28]
    S. C. Cramer, S. P. Finklestein, J. D. Schaechter, G. Bush, and B. R. Rosen, "Activation of distinct motor cortex regions during ipsilateral and contralateral finger movements, " J. Neurophysiol. , vol. 81, no. 1, pp. 383-387, Jan. 1999. http://jn.physiology.org/content/81/1/383
    [29]
    C. Andrew and G. Pfurtscheller, "Event-related coherence as a tool for studying dynamic interaction of brain regions, " Electroencephalogr. Clin. Neurophysiol. , vol. 98, no. 2, pp. 144-148, Feb. 1996. http://www.sciencedirect.com/science/article/pii/0013469495002286
    [30]
    P. Manganotti, C. Gerloff, C. Toro, H. Katsuta, N. Sadato, P. Zhuang, L. Leocani, and M. Hallett, "Task-related coherence and task-related spectral power changes during sequential finger movements, " Electroencephalogr. Clin. Neurophysiol. , vol. 109, no. 1, pp. 50-62, Feb. 1998. http://www.sciencedirect.com/science/article/pii/S0924980X9700074X
    [31]
    M. Le Van Quyen, J. Foucher, J. P. Lachaux, E. Rodriguez, A. Lutz, J. Martinerie, and F. J. Varela, "Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony, " J. Neurosci. Meth. , vol. 111, no. 2, pp. 83-98, Sep. 2001. https://www.researchgate.net/publication/283917917_Comparison_of_Hilbert_and_wavelet_methods_for_the_analysis_of_neuronal_synchrony
    [32]
    N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. A. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, " Proc. Roy. Soc. A Math. Phys. Eng. Sci. , vol. 454, no. 1971, pp. 903-995, Mar. 1998. http://www.jstor.org/stable/53161
    [33]
    N. E. Huang, "Review of empirical mode decomposition, " Proc. SPIE, vol. 4391, pp. 71-80, Mar. 2001. doi: 10.1117/12.421232
    [34]
    J. Wang, G. Z. Xu, J. Wang, S. Yang, and W. L. Yan, "Application of Hilbert-Huang transform for the study of motor imagery tasks, " in Proc. 30th Annu. Int. Conf. the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, 2008, pp. 3848-3851. http://ieeexplore.ieee.org/document/4650049/
    [35]
    N. Caramia, F. Lotte, and S. Ramat, "Optimizing spatial filter pairs for EEG classification based on phase-synchronization, " in Proc. 2014 IEEE Int. Conf. Acoustics, Speech, and Signal Processing, Florence, Italy, 2014, pp. 2049-2053. http://ieeexplore.ieee.org/document/6853959/
    [36]
    B. L. Xu, Y. F. Fu, G. Shi, X. X. Yin, Z. D. Wang, H. Y. Li, and C. H. Jiang, "Enhanced performance by time-frequency-phase feature for EEG-based BCI systems, " Sci. World J. , vol. 2014, Article ID 420561, Jun. 2014. http://europepmc.org/articles/pmc4087262/
    [37]
    L. Zhang, W. He, C. H. He, and P. Wang, "Improving mental task classification by adding high frequency band information, " J. Med. Syst. , vol. 34, no. 1, pp. 51-60, Feb. 2010. doi: 10.1007/s10916-008-9215-z
    [38]
    H. L. Li, J. Wang, B. Deng, and X. L. Wei, "Feature extraction method based on AAR model and accumulated band power, " J. Tianjin Univ. (Sci. Technol. ), vol. 46, no. 9, pp. 784-790, Sep. 2013. http://en.cnki.com.cn/Article_en/CJFDTotal-TJDX201309004.htm
    [39]
    S. Y. Yang, Pattern Recognition and Intelligent Computing:Matlab Technology Realization. 2nd ed. Beijing:Publishing House of Electronics Industry, 2011.
    [40]
    Z. Z. Luo, Y. F. Li, M. Meng, and Y. Yao, "EEG feature extraction algorithm based on chaos analysis and wavelet packet transform, " Chin. J. Sci. Instrum. , vol. 32, no. 1, pp. 33-39, Jan. 2011. http://en.cnki.com.cn/Article_en/CJFDTotal-YQXB201101007.htm
    [41]
    X. Y. Wang, J. Jin, Y. Zhang, and B. Wang, "Brain control: humancomputer integration control based on brain-computer interface approach, " Acta Automat. Sin. , vol. 39, no. 3, pp. 208-221, Mar. 2013. http://en.cnki.com.cn/Article_en/CJFDTOTAL-MOTO201303004.htm
    [42]
    Y. Gu, O. F. do Nascimento, M. F. Lucas, and D. Farina, "Identification of task parameters from movement-related cortical potentials, " Med. Biol. Eng. Comput. , vol. 47, no. 12, pp. 1257-1264, Dec. 2009. http://europepmc.org/abstract/MED/19730913
    [43]
    O. F. do Nascimento, K. D. Nielsen, and M. Voigt, "Movement-related parameters modulate cortical activity during imaginary isometric plantarflexions, " Exp. Brain Res. , vol. 171, no. 1, pp. 78-90, May 2006. http://www.ncbi.nlm.nih.gov/pubmed/16320044
    [44]
    Y. F. Fu, B. L. Xu, Y. C. Li, Y. C. Wang, Z. T. Yu, and H. Y. Li, "Single-trial decoding of imagined grip force parameters involving the right or left hand based on movement-related cortical potentials, " Chin. Sci. Bull. , vol. 59, no. 16, pp. 1907-1916, Jun. 2014. doi: 10.1007/s11434-014-0234-5
    [45]
    Y. F. Fu, B. L. Xu, Y. C. Li, H. Y. Li, Y. C. Wang, and Z. T. Yu, "Recognition of actual grip force movement modes based on movementrelated cortical potentials, " Acta Automat. Sin. , vol. 40, no. 6, pp. 1045-1057, Jun. 2014. http://en.cnki.com.cn/Article_en/CJFDTOTAL-MOTO201406002.htm
    [46]
    R. Palaniappan, "Brain computer interface design using band powers extracted during mental tasks, " in Prco. 2nd Int. IEEE EMBS Conf. Neural Engineering, Arlington, VA, USA, 2005, 321-324. http://www.academia.edu/3643628/Brain_Computer_Interface_Design_Using_Band_Powers_Extracted_During_Mental_Tasks
    [47]
    Y. X. Wang, T. S. Qiu, R. Liu, C. Y. Li, and Z. Ma, "Dynamic motor imagery classification with signal power projection based feature, " Signal Proc. , vol. 28, no. 8, pp. 1059-1062, Aug. 2012. http://en.cnki.com.cn/Article_en/CJFDTotal-XXCN201208002.htm

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